Insights Series: AI Skills
On this page, you will find our series of insights on AI skills.
Findings from the Future of AI Skills in Investment Management Conference
The shift from “Talent Pyramid” to “Talent Diamond” is no longer a forecast – it’s happening.
John Paul, CEO of equitably.ai was recently on a panel with brilliant early-career executives from Barclays and Nokia at the Investment Association‘s Investment 20/20: Talent, Apprenticeships and Skills Conference, to discuss the impact of AI skills on jobs of the future in investment management.
The conference kindly hosted at Aberdeen and sponsorded by Fitch Learning was packed with insights from learning and development, talent and human resources professionals representing: Aberdeen, AllianceBernstein, Amundi, Aon, Artemis Fund Managers, Aviva Investors, Barclays, BNP Paribas Asset Management, BNY, Charles Stanley & Co, the Chartered Institute of Personnel and Development (CIPD), Columbia Threadneedle Investments, Fidelity International, Janus Henderson, Legal & General Asset Management, M&G, National Australia Bank, Northern Trust Asset Management, Polar Capital LLP, Premier Miton Investors, Rathbones, Schroders, State Street, and Vanguard.
John Paul shared data on how AI and the need for AI skills are fundamentally reshaping recruitment and operational structures in investment management and financial services, from pyramid – higher juniors on the bottom – to diamond – more reskilling in the middle – and AI skills being the key reskilling in the middle of the diamond. One of the most pressing points we addressed was research I shared from the US that shows a 16% decline in relative employment for early-career workers in AI-exposed roles, while experienced roles remain stable.
In short, there is evidence that some firms are freezing the entry-level pipeline because they aren’t sure how AI will impact work and are unable to plan for integrating juniors.
There were some great presentations covering skills in 2030 and predictions that core skills will shift heavily toward AI skills, big data, and technological literacy, but they must be balanced with human strategic judgment and governance/ethics. There will be a shift from managers managing people to orchestrating blended systems of humans and AI agents.
John Paul put forward a path that can help move the narrative from displacement to redeployment for early careers. At equitably.ai, we believe that if you approach AI with a robust, quality-first governance programme framework, it can provide the confidence and ROI to not just develop AI skills and protect existing talent but also create a path for early career and accelerate AI with innovation and energy that early talent can provide.
Quality AI talent framework: research and key takeaways from the conference
- Hire for T-Shaped Fluency: Combine deep domain expertise (Asset Class Mastery) with Tech Fluency (Prompt Engineering and Data Literacy). The wage premium for AI specialists in finance has already jumped from 25% in 2024 to 56% in 2025. The market is pricing in the value of those who can govern these systems effectively.
- Manage the ‘Jagged Frontier’: Human performance drops 19% when trusting AI outside its capabilities. Robust quality assurance is critical to prevent “cognitive atrophy” in juniors who may offload too much traditionally manual analysis.
- Bridge the Manager-Analyst Disconnect: Recent data shows a gap in expectations; while 70%+ of investment professionals feel they need more support in AI adoption, L&D leaders must provide more than just tools and provide a roadmap for resilience and personal agility.
- Embrace Apprenticeship-Led Innovation: A structured programme-based approach to AI provides the governance and clarity that firms need to plan for and integrate early-career individuals through apprenticeships in AI and data science. By placing them on governed AI projects, firms can gain the “silicon-native” energy and innovation young people bring, while they learn the “tacit knowledge” only seniors can provide for long-term talent and leadership development.
Although AI will disrupt and impact all sectors, there was much debate at the conference about business, moral, societial duty to work and support the next generation so AI isn’t seen as only a threat but also an opportunity when backed up with governance that can provide the guardrails, and ROI for their integration.
Is your firm thinking about a transition from a Talent Pyramid to a Talent Diamond and how AI skills can support the transition? Contact us and read on below for more on AI skills.
AI Skills Report
What AI roles , what AI skills? are the most common questions I come across – after where do I deploy AI! – as I speak with executives about AI, deliver AI learning and discuss AI with bodies ranging from the Chartered Quality Institute, the Australian Marketing Institute, the Investment Association or the United Nations AI Skills Coalition.
To help shed some light on this important and fast-changing area, I am pleased to share the first issues of the AI Skills Report for Q3 2025 from equitably.ai.
A revealing insight from this issue for me has been the emerging race between growth-oriented roles like AI Product Managers that derive their skills from my marketing profession, and on the flip side, the more guardrail-type roles like AI Governance, more aligned to my quality profession. Given I am part of both professions, it is a bit like my watching rugby between England and Australia as a British-Australian!
I’ve been applying, writing and talking about the application of the Chartered Quality Institute’s professional map/quality competencies to AI – see my article in Quality World here – for some time. The competencies include not only the much talked about governance of AI, but also the context, leadership, improvement and assurance of AI that, when skillfully applied together by professionals are in my opinion currently the best way to maximise the impact of AI while minimising risks.
I hope my fellow professionals in marketing and quality, along with all those interested in getting the most from AI and building career resiliance in the age of disruptive AI, find coverage of AI skills and AI roles interesting. Feel free to contact me if you have any comments or queries or are interested in the AI Working Group I lead at the Chartered Quality Institute here.
John Paul Danaee, Chartered Marketer, Chartered Quality Professional, Fellow of the Australian Marketing Institute
7 Key AI Roles & AI Skills You Need to Know for You Career
Key Findings: AI Governance Roles Explode!
Top 4 AI roles:
- ML Engineering & ML Ops
- AI Governance/Risk/Compliance/Quality (see chart for skills)
- Data Science
- AI Product Management (see chart for skills)
Fastest-growing roles: AI Governance (see chart for skills) leads the pack. Almost every major UK bank now has some kind of AI governance function under titles such as responsible AI. EU AI Act compliance is driving a 2025 governance surge
The platform imperative: MLOps and ML Engineering dominate technical hiring. Organisations are building centralised AI/ML platforms to serve distributed product teams.
Technical AI skills: Azure OpenAI dominates UK enterprise GenAI, followed by AWS Bedrock. RAG, LangChain, and prompt engineering are now baseline skills for GenAI engineers.
Organisational model: Centre of Excellence (CoE) wins as dominant structure. One of the major banks exemplifies this with an AI CoE in the Chief Data & Analytics Office, combining centralised governance with federated delivery. Platform teams provide infrastructure whilst embedded teams execute.
Chart: AI Role Demand (per sample)
Chart: Skills for AI Governance/Risk/Compliance/Quality
7 Key AI Roles Professionals Need to Know About
1. AI Programme/Project Management
Definition: Strategic leaders managing AI transformation initiatives, programme delivery, and cross-functional AI adoption.
Key responsibilities: Define AI programme elements and KPIs, identify, score and manage AI project portfolios, translate business requirements into technical and coordinate with technical teams, vendor management, and delivery of AI use cases at scale.
Synonyms captured: AI Programme Manager, AI Project Manager, AI Delivery Lead, AI Transformation Lead, AI Accelerator Lead
2. AI Product Management – SEE CHART FOR SKILLS
Definition: Product managers specialising in AI/ML products, defining roadmaps, prioritising AI features, and bridging user needs with ML capabilities.
Key responsibilities: AI product strategy, feature prioritisation, working with ML/data science teams, stakeholder management, GenAI product development.
Synonyms captured: AI Product Manager, GenAI Product Owner, ML Product Manager, AI/ML Product Manager.
Chart: Skills for AI Product Management
3. AI Governance/Risk/Compliance – FASTEST GROWING
Definition: Specialists who use frameworks to ensure that AI systems deployed meet regulatory, ethical, quality and organisational standards.
Key responsibilities: Develop AI governance frameworks, ensure regulatory compliance (EU AI Act, UK FCA, UK ICO), model risk management, establish ethical AI principles, conduct AI audits, and implement guardrails.
Synonyms captured: Responsible AI Lead, AI Governance Manager, Model Risk Manager, AI Policy Manager, AI Compliance Manager, AI Assurance Manager, AI Ethics Lead, AI Controls & Governance Advisor.
4. AI/ML Architecture
Definition: Architects designing end-to-end AI/ML solution architectures, infrastructure patterns, and technical blueprints for AI systems.
Key responsibilities: Design AI/ML architectures, select technology stacks, establish technical standards, guide solution implementation, cloud AI platform architecture.
Synonyms captured: AI Architect, ML Architect, Data/AI Solution Architect, Lead AI Architect, GenAI Architect.
5. ML Engineering & MLOps – HIGHEST NUMBER
Definition: Engineers building, deploying, and operationalising ML models. Focus on production ML systems, infrastructure, monitoring, and MLOps practices.
Key responsibilities: Build ML pipelines, deploy models to production, implement MLOps practices, model monitoring and maintenance, ML infrastructure development etc.
Synonyms captured: ML Engineer, Machine Learning Engineer, MLOps Engineer, ML Platform Engineer, LLMOps Engineer, Senior ML Engineer, GenAI Platform Engineer.
6. Prompt Engineer – GROWING SPECIALISATION
Definition: Engineers specialising in Large Language Models, generative AI applications, prompt engineering, and LLM-based systems.
Key responsibilities: Design and implement LLM applications, prompt engineering, RAG implementation, LLM fine-tuning, build AI agents, LLM evaluation and guardrails.
Synonyms captured: GenAI Engineer, LLM Engineer, Prompt Engineer, Conversational AI Engineer, AI Engineer (GenAI focus), Conversational Designer.
7. AI Change & Adoption
Definition: Change managers and enablement specialists driving AI adoption, upskilling employees, and managing organisational, resistance, cultural change and transformation for AI.
Key responsibilities: AI adoption strategy, change management, AI literacy and training programmes, stakeholder engagement, skills development, and AI enablement.
Synonyms captured: Change Manager (AI), AI Enablement Lead, AI Adoption Lead, AI Skills Development Lead, AI Transformation.
Understand and Unlock Your AI Competency
If you have any comments, or are interested in the full report – not all roles and their skills are covered here – or would like to discuss your personal or team’s AI competency along with what are the best paths for AI learning and development, the author would be happy to have a chat and can be reached via our Contact Page.
Disclaimer: This report does not constitute legal or professional advice. Advanced, research-specialised AI models have been used in research for the report. Although the report has been reviewed by human experts in the field, it may contain inaccuracies, and the author and Equitably AI Limited do not warrant its accuracy or completeness.